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2026/2027  BA-BINTV1051U  Big Data Analytics for Managers

English Title
Big Data Analytics for Managers

Course information
Language English
Course ECTS 7.5 ECTS
Type Elective
Level Bachelor
Duration One Semester
Start time of the course Autumn
Timetable Course schedule will be posted at calendar.cbs.dk
Max. participants 140
Study board
Study Board for Professions
Programme BSc in Business Administration and Information Systems
Course coordinator
  • Coen van der Geest - Department of Digitalisation (DIGI)
Main academic disciplines
  • Managerial economics
  • Information technology
  • Statistics and quantitative methods
Teaching methods
  • Blended learning
Last updated on 26-01-2026

Relevant links
Learning objectives
To achieve the grade 12, students should meet the following learning objectives with no or only minor mistakes or errors:
  • Demonstrate understanding of the fundamental concepts and technologies used in big data analytics
  • Analyze and formulate a real-world business or societal problem suitable for investigation using big data analytics
  • Conduct desk-based research by identifying, collecting, and processing secondary data to investigate a defined business or societal problem
  • Apply data exploration and visualization techniques to interpret patterns, trends, and insights related to the business or societal problem
  • Apply and evaluate supervised and/or unsupervised learning methods to address the formulated problem
  • Compare and contrast big data analytics methods with respect to their alignment with business or societal objectives, strengths, and limitations
Course prerequisites
Basic statistics and great interest in quantitative analysis.
Programming skills are not required.
Examination
Big Data Analytics for Managers:
Exam ECTS 7,5
Examination form Home assignment - written product
Individual or group exam Individual exam
Size of written product Max. 15 pages
Assignment type Project
Release of assignment An assigned subject is released in class
Duration Written product to be submitted on specified date and time.
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Winter
Make-up exam/re-exam
Same examination form as the ordinary exam
- Students who are doing the re-exam have to choose their own topic and the relevant data. The provided datasets on Canvas can not be used for the re-exam anymore.
Description of the exam procedure

- Students will work on a self-selected topic with relevant self established datasets, using predefined tools. There is also an option that students can choose from a range of provided topics and datasets. There will be four weeks for the students to do the project and submit the written product.

 
Course content, structure and pedagogical approach

This course equips students with the knowledge and skills to leverage big data analytics in practice. It covers key concepts, methods, and tools from a managerial perspective, emphasizing how to define big data analytics projects, collect, process, analyze, visualize, report, and evaluate big data to create business and societal value. Students will be introduced to all stages of the big data analytics project cycle, from definition to evaluation and recommendation.

 

Through case-based workshops and hands-on exercises utilizing industry relevant and practical tools, students will learn to translate data into action. The managerial perspective, and the hands-on exercises, ensure that students obtain both practical and theoretical understanding in how to setup and execute a big data analytics project effectively.

 

Course topics are listed below:

  • Foundations: concepts, models and exemplary cases
  • Working with data
  • Unsupervised big data methods and tools
  • Supervised big data methods and tools
  • Visual analytics and tools
  • Model evaluation
  • Applications and ethics
Research-based teaching
CBS’ programmes and teaching are research-based. The following types of research-based knowledge and research-like activities are included in this course:
Research-based knowledge
  • Classic and basic theory
  • Methodology
  • Models
Research-like activities
  • Data collection
  • Analysis
Description of the teaching methods
Through a blended learning approach, students have access to online pre-recorded knowledge clips, and in-person practical exercises and case-based workshops. Using industry-relevant tools, students will develop skills to analyze complex data and to translate data into action.
Feedback during the teaching period
- Feedback to hands-on exercises will be given collectively every week based on students' questions.
- Discussion boards on Canvas will be available for general questions.
- Q&A regarding the final project will be arranged in the final session and in a dedicated discussion board.
- Individual consultation can be arranged during consultation hours.
Student workload
Lectures 30 hours
Exercise and Case Workshops 20 hours
Lecture Preparation and Reading 60 hours
Workshop Preparation 20 hours
Individual Exam Project: Work and Report 76 hours
Total 206 hours
Expected literature

The literature can be changed before the semester starts. Students are advised to find the final literature on Canvas before purchasing the books.

 

 
Last updated on 26-01-2026